This descriptor layer identifies types of roads, core urban areas, and energy production sites for each impervious pixel to allow deeper analysis of developed features. Accurately mapping a small number of new urban areas doesn’t lend itself well to automated classification, as patches of trees and bare urban areas can so easily be tagged as forest or bare ground. “We were able to go in there through Denali and take what we needed.”. The latest edition of the National Land Cover Database (NLCD 2016) for Alaska is now publicly available. This descriptor layer identifies types of roads, core urban areas, and energy production sites for each impervious pixel to allow deeper analysis of developed features. For example, building coverage ratio increased as a power function of the percentage of the impervious surface area as estimated in NLCD (Fig. 30 meter raster, with cells classified into one of 21 land cover classes. NLCD 2016 also offers an impervious surface descriptor product. The National Land Cover Database (NLCD) 2016 product suite was recently released for bulk download on the Multi-Resolution Land Characteristics Consortium (MRLC) website. NLCD provides additional geospatial information for the country and involves partner products such as tree canopy, urban imperviousness, and western U.S. shrub and grassland area at varying periodicities. An approach for mapping large-area impervious surfaces: Synergistic use of Landsat 7 ETM+ and high spatial resolution imagery. impervious_descriptor Defines which impervious layer pixels are roads and provides the best fit description for impervious pixels that are not roads. The three largest countries in North America share trade, climate and culture in a host of broad and specific ways. Imperviousness data from the National Land Cover Dataset (NLCD). No new imperviousness products for Alaska, Hawaii and Puerto Rico are available from NLCD 2016. The initial runoff from paved surfaces can reach extremely high temperatures “What we’re trying to do right now for NLCD is kind of unprecedented – doing a 2019 NLCD for release in 2020,” said Terry Sohl, a research physical scientist at the USGS Earth Resources Observation and Science (EROS) Center who recently took on the role of NCLD co-director. NLCD from 2001 to 2016 (Animation) Attribute Accuracy: A formal accuracy assessment has not been conducted for NLCD 2016 Land Cover, NLCD 2016 Land Cover Change, or NLCD 2016 Impervious Surface products. With the 2016 release, the intended snapshot interval is 2-3 years, beginning with 2001. The current Landsat ARD tiles used for LCMAP’s work now serve as a starting point for NLCD 2019’s compositing work. 4. - A new “impervious descriptor” data layer for each of those years to show the footprint of wind energy in the U.S.. - Improved accuracy through the integration of decades of forest regrowth data from LANDFIRE and Landsat Analysis Ready Data (ARD) tiles generated for the USGS Land Change Monitoring, Assessment, and Projection (LCMAP) initiative. The new production techniques have helped free up time for the detailed impervious surface products that are unique to the NLCD product suite. The “Impervious Descriptor” data layer offers the first and only nationwide, Landsat-scale map of roadways and oil production NLCD released its most expansive and detailed database to date in mid-2019, sorting each 30-meter plot of ground in the country into a land cover class – e.g. “Being able to kick out a national scale map within a year is pretty impressive.”. 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